{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bdbd4017-d283-4e82-ab5c-41be4d1902f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cc667eb7-5acd-44b1-a9d1-a7b486086e73",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d77939c7-3a0b-4b52-a9e7-31c8d96a7513",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
    "raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
    "data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
    "target = raw_df.values[1::2, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96a89c7-e573-4b2d-9433-45626444a94d",
   "metadata": {},
   "source": [
    "## 一、特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78b5d403-54e1-40ad-b0db-183dbd83192a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.copy()\n",
    "y = target.copy()\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c39ce472-4f6c-4840-a23c-3804434d7212",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用numpy库的concatenate函数，将全1数组（形状为(506,1)）与X进行横向拼接，并将结果赋值给X\n",
    "X = np.concatenate((X, np.ones(shape=(X.shape[0], 1))), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10cbfe95-d4e4-407f-9293-1e6de7545c69",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b7ff0c9-9996-427a-ae65-2ff6bb941aab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(102, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fea20283-fdf2-40a5-a24e-33665e1308c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(102,)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d21b4be3-ded0-4092-81a1-fa825604a126",
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = ['crim','zn','indus','chas','nox','rm','age','dis','rad','tax','ptradio','b','lstat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "35ddb208-8ac9-4c3c-8370-78d15d2e7740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptradio</th>\n",
       "      <th>b</th>\n",
       "      <th>lstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      crim    zn  indus  chas    nox     rm   age     dis  rad    tax  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   ptradio       b  lstat  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_df = pd.DataFrame(data=data, columns=feature_names)\n",
    "house_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "013a7885-a008-48a8-ac6e-163ac4e94863",
   "metadata": {},
   "source": [
    "## 二、线性回归正规方程求解"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3ad3822-9e0e-4eba-a49e-560b0df36db7",
   "metadata": {},
   "source": [
    "$$\n",
    "\\theta = (X^TX)^{-1}X^Ty\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bc80fb1b-4a0d-4a3b-abcc-176459588046",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = np.linalg.inv(X_train.T.dot(X_train)).dot(X_train.T).dot(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "80168cb7-49c0-4d89-b0a3-2409cf4eecee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.87655644,  1.07293926, -0.08713625,  0.74059517, -1.67957774,\n",
       "        3.05106013, -0.56954433, -3.18522123,  1.87696934, -1.93774574,\n",
       "       -1.83357079,  0.9455599 , -2.89280636, 22.22124102])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b75c3519-df45-4362-82fb-429b678469a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.87655644,  1.07293926, -0.08713625,  0.74059517, -1.67957774,\n",
       "        3.05106013, -0.56954433, -3.18522123,  1.87696934, -1.93774574,\n",
       "       -1.83357079,  0.9455599 , -2.89280636])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([22.22124102])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "w = theta[:-1]\n",
    "b = theta[[-1]]\n",
    "display(w, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de25a7eb-7a4e-4112-b793-169f568972d6",
   "metadata": {},
   "source": [
    "## 三、特征选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "addf0767-fd98-4b89-9dc3-f3c20e18c822",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4*5, 4*5))\n",
    "\n",
    "for i, col in enumerate(feature_names, start=1):\n",
    "    X = house_df[[col]].values\n",
    "    linear = LinearRegression()\n",
    "    linear.fit(X, target)\n",
    "    X_test = np.linspace(X.min(), X.max(), num=100).reshape(-1, 1)\n",
    "    y_ = linear.predict(X_test)\n",
    "    axes = plt.subplot(4, 4, i)\n",
    "    axes.scatter(X, target)\n",
    "    axes.plot(X_test, y_, c='r')\n",
    "    axes.set_title(f'feature:{col}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5f1f159-13dd-4432-89cd-2a1402b49a88",
   "metadata": {},
   "source": [
    "## 四、批量梯度下降算法（BGD）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "692cf34c-717a-4b55-a28b-56e8d5864eab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机初始化参数\n",
    "theta = np.random.randn(14, 1)\n",
    "\n",
    "# 定义学习率\n",
    "eta = 0.0005\n",
    "\n",
    "# 初始化损失和上一次损失\n",
    "loss = 0\n",
    "last_loss = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79553cdd-1ee0-4582-b34f-f90c502b09cd",
   "metadata": {},
   "source": [
    "梯度计算公式是通过矩阵运算得到的:\n",
    "\n",
    "- 计算预测值：X_train.dot(theta)\n",
    "- 计算误差：y_train.reshape(404,1) - X_train.dot(theta)\n",
    "- 计算梯度：X_train.T.dot(X_train.dot(theta)-y_train.reshape(404,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0dcf6fd4-136e-49d8-a8b6-4fa38242b512",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 14)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape  # 意味着索引是 0到 403"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ef226870-68af-4c26-9e88-2fc4e7c1e8ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 1)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd66f451-b875-44f2-85e9-78e1f14f5c18",
   "metadata": {},
   "source": [
    "首先定义了损失函数梯度g的计算。这里的损失函数用的是均方误差（MSE），它是预测值与真实值之间的平方和的均值。对于线性回归模型$y=X\\theta$（其中$X$是特征矩阵，$\\theta$是模型参数向量，$y是目标向量$）MSE可以写作$\\frac{1}{2n}\\left \\| X\\theta-y \\right \\|^2$，其梯度（即损失函数关于$\\theta$的导数）为$X^T(X\\theta-y)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aae8c866-a547-4aff-a08e-e3f18fcf4bab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:159257.21708890187\n",
      "loss:7391.460768638754\n",
      "loss:7383.349686429309\n",
      "loss:7382.742936995375\n",
      "loss:7382.694246657431\n"
     ]
    }
   ],
   "source": [
    "# 迭代1000次\n",
    "for i in range(2000):\n",
    "    # 如果损失值的变化小于0.00001，则提前终止迭代\n",
    "    if np.abs(loss - last_loss) < 1e-5:\n",
    "        break\n",
    "    # 计算梯度g，BGD特点：使用所有的训练数据来计算梯度\n",
    "    g = X_train.T.dot(X_train.dot(theta)-y_train.reshape(-1,1))\n",
    "    # 更新theta参数\n",
    "    theta = theta - eta*g\n",
    "    # 更新上一轮损失数值last_loss为当前损失值\n",
    "    last_loss = loss\n",
    "    # 更新当前损失，使用训练数据和参数计算预测值与实际值的差值，然后平方求和\n",
    "    loss = ((X_train.dot(theta) - y_train.reshape(-1, 1))**2).sum()\n",
    "    if i % 100 == 0:\n",
    "        print(f'loss:{loss}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fdf5d4eb-124d-43a3-9040-b18096bd9ebf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.87639318],\n",
       "       [ 1.07255279],\n",
       "       [-0.08820932],\n",
       "       [ 0.74074084],\n",
       "       [-1.67946457],\n",
       "       [ 3.05128611],\n",
       "       [-0.56972206],\n",
       "       [-3.18533191],\n",
       "       [ 1.87451539],\n",
       "       [-1.93492853],\n",
       "       [-1.83350817],\n",
       "       [ 0.94553196],\n",
       "       [-2.89260487],\n",
       "       [22.2212482 ]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f538a97-5077-46a8-aae4-08fb0f9fc0d2",
   "metadata": {},
   "source": [
    "## 五、随机梯度下降算法（SGD）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "dd41b557-06bf-48a4-a9ea-a699f699423d",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = np.random.randn(14, 1)\n",
    "t0 = 1\n",
    "t1 = 1000\n",
    "t = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "dece695b-663c-4b48-9ac4-7cd0583eecdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def learn(t):\n",
    "    return t0 / (t + t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d671e167-820c-45e7-967f-d969f6d2714e",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = 0\n",
    "last_loss = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c7cf0541-dabb-46d1-b05d-2c5b7a93bf4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([300])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 从0到403（包括0和403）之间随机选择一个整数，生成一个一维数组\n",
    "np.random.choice(404, size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "e15151e6-6009-4bfc-a19a-ac4f33ea2661",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置迭代次数上限\n",
    "for i in range(2000):\n",
    "    # 如果损失值变化小于阈值，则停止迭代\n",
    "    if np.abs(loss - last_loss) < 1e-7:\n",
    "        break\n",
    "    # 随机选择一个样本\n",
    "    index = np.random.choice(404, size=1)\n",
    "    X_i = X_train[index]  # X_train.shape 是 (404, 14)\n",
    "    y_i = y_train[index]\n",
    "    # 计算梯度\n",
    "    g = X_i.T.dot(X_i.dot(theta) - y_i)\n",
    "    # 更新学习率\n",
    "    eta = learn(t)\n",
    "    # 更新参数\n",
    "    theta = theta - eta*g\n",
    "    # 更新损失函数值\n",
    "    last_loss = loss\n",
    "    loss = ((X_i.dot(theta) - y_i)**2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4d4c281f-dc15-4c70-95ac-ca0364a300a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2],\n",
       "       [2, 1]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 随机一个从[0, 5)中取值，size=(2, 2)的矩阵\n",
    "np.random.choice(5, size=(2, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "9c96b38f-42c0-4b18-8ff1-6949f4a085ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.48242719],\n",
       "       [-0.02305249],\n",
       "       [-1.08076809],\n",
       "       [ 1.44044761],\n",
       "       [-0.61221533],\n",
       "       [ 3.16150137],\n",
       "       [-0.82075964],\n",
       "       [-1.8383025 ],\n",
       "       [ 0.4263733 ],\n",
       "       [-0.23368445],\n",
       "       [-1.86397758],\n",
       "       [ 0.67486518],\n",
       "       [-2.9515222 ],\n",
       "       [19.6615836 ]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d106b5d3-fbb0-47fd-9e4a-d8b0d9ade558",
   "metadata": {},
   "source": [
    "## 六、小批量梯度下降算法（MBGD）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b421b24b-8091-4766-8799-b7186a67f275",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 初始化参数theta，eta，损失值loss，上一轮损失值last_loss，批量大小break_size\n",
    "theta = np.random.randn(14, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "2509e76f-5a47-42cc-a20d-3dfd54b29591",
   "metadata": {},
   "outputs": [],
   "source": [
    "eta = 0.0001\n",
    "loss = 0\n",
    "last_loss = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "43762b52-b384-4974-9555-f3a85f662f5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "29729d3a-3f52-4c06-a6b6-b7d22f2d98d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d85a40e-abdc-4f14-b33d-8e4f8873a71b",
   "metadata": {},
   "source": [
    "np.random.shuffle(arr) 和 np.random.permutation(arr) 都是NumPy库中用于随机重排数组元素的函数，但它们之间存在一些关键区别：\n",
    "\n",
    "1、in-place操作 vs 返回新数组：\n",
    "\n",
    "- np.random.shuffle(arr)：这个函数会就地修改输入的数组arr，直接对其进行随机重排，不返回任何值（返回None）。原数组的元素顺序会被永久改变。\n",
    "- np.random.permutation(arr)：这个函数不会修改输入的数组，而是返回一个新的数组，该数组包含输入数组的元素，但顺序是随机排列的。原数组保持不变。\n",
    "\n",
    "\n",
    "2、输入灵活性：\n",
    "\n",
    "- np.random.shuffle(arr)：要求输入的arr是一个一维数组或者是一个可迭代对象（在实践中，通常是一维数组）。它不能直接应用于多维数组的整个结构上进行shuffle。\n",
    "- np.random.permutation(arr)：更加灵活，可以接受一维数组、整数或者一个多维数组作为输入。如果输入是整数n，则返回一个从0到n-1的随机排列数组；如果是多维数组，它会首先将多维数组扁平化为一维，然后再进行随机排列，但最终返回的仍然是与原数组形状相同的排列结果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "f8be92fe-4e2d-43cc-becd-dd3e935ee2e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 9, 2, 5, 1, 4, 8, 7, 6, 3])"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffle会就地修改输入的数组\n",
    "np.random.shuffle(arr)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "15a1237b-fdde-46e1-b631-793ba8494d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 2, 1, 0, 6, 9, 4, 5, 3, 7])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# permutation返回一个新的数组，该数组包含输入数组的元素，但顺序是随机排列的。原数组保持不变。\n",
    "np.random.permutation(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "7f7bef01-b7cf-4702-adb2-7a910664ae1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss:602.4040227484167\n",
      "loss:598.1484840545108\n",
      "loss:778.3738032835249\n",
      "loss:476.57962638145034\n",
      "loss:497.6360235262325\n",
      "loss:491.97074296005223\n",
      "loss:471.6563377232094\n",
      "loss:404.8122349597404\n",
      "loss:592.6572850860276\n",
      "loss:501.98807433328653\n",
      "loss:451.4236484142788\n",
      "loss:398.3842040615193\n",
      "loss:333.32353747947826\n",
      "loss:400.5461846415296\n",
      "loss:359.39186741891956\n",
      "loss:379.7946332553499\n",
      "loss:376.6975335873087\n",
      "loss:373.4331436457978\n",
      "loss:297.5957995980375\n",
      "loss:436.14237376254175\n",
      "loss:329.36263968942944\n",
      "loss:245.23339517370394\n",
      "loss:373.3849021302159\n",
      "loss:273.12761177078255\n",
      "loss:258.8289058023844\n"
     ]
    }
   ],
   "source": [
    "# 迭代5000次\n",
    "for i in range(5000):\n",
    "    # 如果损失值变化小于1e-7，跳出循环\n",
    "    if np.abs(loss - last_loss) < 1e-7:\n",
    "        break\n",
    "    # 随机选择一个小批量样本索引\n",
    "    batch_indices = np.random.choice(len(X_train), size=batch_size, replace=False)\n",
    "    # 根据批量索引获取训练数据和标签\n",
    "    X_batch = X_train[batch_indices]\n",
    "    y_batch = y_train[batch_indices]\n",
    "    # 计算小批量的梯度\n",
    "    gradients_sum = np.zeros_like(theta)\n",
    "    for Xi, yi in zip(X_batch, y_batch):\n",
    "        gradients_sum += ((Xi.dot(theta) - yi) * Xi.reshape(-1, 1))\n",
    "    g = gradients_sum / batch_size\n",
    "    \n",
    "    # 更新参数theta\n",
    "    theta = theta - eta*g\n",
    "    # 更新上一轮损失值\n",
    "    last_loss = loss\n",
    "    # 更新当前轮的损失，这里也可以计算小批量的平均损失代替单个样本的损失累加\n",
    "    loss = np.sum((X_batch.dot(theta) - y_batch.reshape(-1, 1))**2) / batch_size\n",
    "    if i % 200 == 0:\n",
    "        print(f'loss:{loss}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "a138df4e-3d69-4820-b03a-999b5de75b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.97791063],\n",
       "       [ 1.50657834],\n",
       "       [-1.32540611],\n",
       "       [ 1.46984391],\n",
       "       [-0.68092868],\n",
       "       [ 1.51753931],\n",
       "       [ 0.28468972],\n",
       "       [ 0.39928149],\n",
       "       [-0.34171754],\n",
       "       [-0.30331048],\n",
       "       [-0.45249493],\n",
       "       [ 1.35114108],\n",
       "       [-1.0528564 ],\n",
       "       [ 7.26024179]])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "108c45a0-bd09-49e5-9a1b-83824e16bb9d",
   "metadata": {},
   "source": [
    "## 七、选择一个特征来可视化展示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "10f4db64-b2aa-466d-b9e0-52a486ae7c7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = house_df['rm'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "d218e223-57f3-4bd0-a9f9-13bbd275e780",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "b7028480-9c27-4253-ae8a-58ba80e6055b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "d8343369-65ca-4578-81ae-8d9e5a50a9c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 1)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "68aed271-adc7-46d1-aba8-9e9fbb465b66",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.concatenate((X_train, np.ones_like(X_train)), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "dd941e7e-3a55-4772-9b5a-484fbe3ae6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.575, 1.   ],\n",
       "       [6.421, 1.   ],\n",
       "       [7.185, 1.   ],\n",
       "       ...,\n",
       "       [6.976, 1.   ],\n",
       "       [6.794, 1.   ],\n",
       "       [6.03 , 1.   ]])"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca65b1e-ede7-4801-ad5b-080120bd2253",
   "metadata": {},
   "source": [
    "### BDG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "ed85c6b0-ae80-449f-8211-234996076a48",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506,)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "0122768a-ae9a-41b9-8c4d-b2edc3e55b91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24. ],\n",
       "       [21.6],\n",
       "       [34.7],\n",
       "       [33.4],\n",
       "       [36.2],\n",
       "       [28.7],\n",
       "       [22.9],\n",
       "       [27.1],\n",
       "       [16.5],\n",
       "       [18.9],\n",
       "       [15. ],\n",
       "       [18.9],\n",
       "       [21.7],\n",
       "       [20.4],\n",
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       "       [11.9]])"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "81386eff-6024-4097-8aeb-33670fd19db4",
   "metadata": {
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   },
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       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "938d5469-8c1c-4a8f-82ae-c842864a0d5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置画布大小\n",
    "plt.figure(figsize=(6, 5))\n",
    "\n",
    "# 初始化参数\n",
    "theta = np.random.randn(2, 1)  # 随机生成初始参数\n",
    "eta = 1e-5  # 学习率\n",
    "loss = 0  # 损失值初始化\n",
    "last_loss = 1  # 上一轮损失值初始化\n",
    "epsilon = 1e-8  # 添加一个小的正数防止除以零的情况\n",
    "\n",
    "# 迭代更新参数\n",
    "for j in range(10):  # 外层循环，进行10次迭代\n",
    "    for i in range(8000):  # 内层循环，每次迭代8000次\n",
    "        # 如果损失值变化小于阈值，则跳出内层循环\n",
    "        if np.abs(loss - last_loss) < 1e-5:\n",
    "            break\n",
    "        # 计算梯度\n",
    "        g = X_train.T.dot(X_train.dot(theta) - target.reshape(-1, 1)) + epsilon\n",
    "        # 更新参数\n",
    "        theta = theta - eta * g\n",
    "        # 更新上一轮损失值\n",
    "        last_loss = loss\n",
    "        # 计算当前损失值\n",
    "        loss = np.sum((X_train.dot(theta) - target.reshape(-1, 1))**2)\n",
    "    # 生成测试数据\n",
    "    X_test = np.linspace(X_train[:, 0].min(), X_train[:, 0].max(), num=1000).reshape(-1, 1)\n",
    "    y_ =  theta[0] * X_test + theta[1]\n",
    "    # 绘制训练数据的散点图\n",
    "    plt.scatter(X_train[:, 0], target)\n",
    "    # 绘制拟合直线\n",
    "    plt.plot(X_test, y_, c='r')\n",
    "\n",
    "# 显示图像\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "c61aff0a-b3fd-442c-baa3-619fc01618ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  9.08934736],\n",
       "       [-34.58944259]])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看模型参数\n",
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb3ae6b8-66e0-4af2-8b96-f610f5c1df8c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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